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Remote Sensing and GIS Technology Applications for Water Resources and Flood Risk Management in River Basin and Coastal Zones

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation for Emergency Management".

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 5095

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Special Issue Editors

Dr. Pierfranco Costabile

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Guest Editor

Department of Environmental Engineering, University of Calabria, 87036 Rende, CS, Italy
Interests: flood propagation; rainfall-runoff modeling; river networks; hazard communication; surface irrigation; impacts of climate change; lidar; soil erosion and sediment transport
Special Issues, Collections and Topics in MDPI journals

Dr. John Kalogiros

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Guest Editor

Institute of Environmental Research and Sustainable Development, National Observatory of Athens, I. Metaxa and V. Pavlou, P. Penteli, 15236 Athens, Greece
Interests: remote sensing; weather radar; precipitation; flood forecasting; atmospheric turbulence; air–sea interaction
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Venkatesh Merwade

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Guest Editor

Lyles School of Civil Engineering, Purdue University, West Lafayette, IN, USA
Interests: surface water hydrology; flood modeling and mapping; cyberinfrastructure for hydrology
Special Issues, Collections and Topics in MDPI journals

Dr. Jochen E. Schubert

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Guest Editor

Department of Civil and Environmental Engineering, University of California Irvine, Irvine, CA 92697, USA
Interests: coastal flood hazards; compound flood hazard modeling; flood risk analysis; flood forecasting; GIS; UAV data collection

Special Issue Information

Dear Colleagues,

The integration of remote sensing and GIS technology has become mainstream in water resource and flood risk management in river basins and coastal zones.

Remote sensing has emerged as a major tool in studying and analysing complex water resources systems, including the mapping of water resources, the monitoring and mapping of floods, and the measurement of hydrologic fluxes. Recent advancements in remote sensing applications were enabled using satellite and unmanned aerial vehicle (UAV) data, photogrammetry, optical and video image classification, radar precipitation measurements and data assimilation. GIS has further advanced the utility of remote sensing data, providing the best tools for processing hydrology data and supporting modelling at different spatial and temporal scales. Finally, the application of data science, machine learning and artificial intelligence is enabling new and unique applications of remote sensing data for solving water resource problems. The application of all of these technologies is expected to have increasing societal benefits in the mitigation of and adaptation to hydro-meteorological extremes within the context of climate change. In particular, data-scarce regions that lack consistent in situ hydrological observations, and are increasingly subjected to climate extremes, will benefit from novel remote sensing technologies and related data products for flood risk management.

The goal of this Special Issue is to collect high-quality and innovative scientific papers describing cutting-edge research on the application of GIS and remote sensing methods from any platform (surface stations, UAV, satellite, aircraft) for water resource modelling and management.

The topics of interest include, but are not limited to, the following:

Satellite and ground-based radar precipitation measurements;
Streamflow measurements;
Water resource mapping;
Land properties mapping;
Flood inundation mapping;
Changing morphology of rivers and coasts for flood hazard management;
Rainfall runoff simulations;
Data assimilation;
Model calibration;
Flood risk management;
Digital elevation models;
Agricultural water management;
Application of data science, machine learning and artificial intelligence.

Dr. Pierfranco Costabile
Dr. John Kalogiros
Prof. Dr. Venkatesh Merwade
Dr. Jochen E. Schubert
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

remote sensing
GIS
precipitation
land properties
streamflow
model calibration
data assimilation
flood inundation
hydrological simulations
flood risk management
water management

Published Papers (4 papers)

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Research

18 pages, 6810 KiB

Open AccessArticle

The Impact of Satellite Soil Moisture Data Assimilation on the Hydrological Modeling of SWAT in a Highly Disturbed Catchment

by Yongwei Liu, Wei Cui, Zhe Ling, Xingwang Fan, Jianzhi Dong, Chengmei Luan, Rong Wang, Wen Wang and Yuanbo Liu

Remote Sens. 2024, 16(2), 429; https://doi.org/10.3390/rs16020429 - 22 Jan 2024

Viewed by 822

Abstract

The potential of satellite soil moisture (SM) in improving hydrological modeling has been addressed in synthetic experiments, but it is less explored in real data cases. Here, we investigate the added value of Soil Moisture and Passive (SMAP) and Advanced Scatterometer (ASCAT) SM data to distributed hydrological modeling with the soil and water assessment tool (SWAT) in a highly human disturbed catchment (126, 486 km²) featuring a network of SM and streamflow observations. The investigation is based on the ensemble Kalman filter (EnKF) considering SM errors from satellite data using the triple collocation. The assimilation of SMAP and ASCAT SM improved the surface (0–10 cm) and rootzone (10–30 cm) SM at >70% and > 50% stations of the basin, respectively. However, the assimilation effects on distributed streamflow simulation of the basin are un-significant and not robust. SM assimilation improved the simulated streamflow at two upstream stations, while it deteriorated the streamflow at the remaining stations. This can be largely attributed to the poor vertical soil water coupling of SWAT, suboptimal model parameters, satellite SM data quality, humid climate, and human disturbance to rainfall-runoff processes. This study offers strong evidence of integrating satellite SM into hydrological modeling in improving SM estimation and provides implications for achieving the added value of remotely sensed SM in streamflow improvement. Full article

(This article belongs to the Special Issue Remote Sensing and GIS Technology Applications for Water Resources and Flood Risk Management in River Basin and Coastal Zones)

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18 pages, 4551 KiB

Open AccessArticle

Combining Satellite Optical and Radar Image Data for Streamflow Estimation Using a Machine Learning Method

by Xingcan Wang, Wenchao Sun, Fan Lu and Rui Zuo

Remote Sens. 2023, 15(21), 5184; https://doi.org/10.3390/rs15215184 - 30 Oct 2023

Cited by 1 | Viewed by 996

Abstract

River water surface extent can be extracted from optical and radar satellite images; this is useful for estimating streamflow from space. The radiation characteristics of open water from the visible and microwave bands are different and provide independent information. In this study, for the purpose of improving streamflow estimation from space for data-sparse regions, a method that combines satellite optical and radar images data for streamflow estimation using a machine learning technique was proposed. The method was demonstratedthrough a case study in the river segment upstream of the Ganzi gauging station on the Yalong River, China. Utilizing the support vector regression (SVR) model, the feasibility of different combinations of water surface area derived from Sentinel-1 synthetic aperture radar images (AREA_SAR), modified normalized difference water index derived from Landsat 8 images (MNDWI), and reflectance ratios between NIR and SWIR channels derived from MODIS images (R_NIR/R_SWIR) for streamflow estimation were evaluated through three experiments. In Experiment I, three models using AREA_SAR (Model 1), MNDWI (Model 2), and a combination of AREA_SAR and MNDWI (Model 3) were built; the mean relative error (MRE) and mean absolute error (MAE) of streamflow estimates corresponding to the SVR model using both AREA_SAR and MNDWI (Model 3) were 0.19 and 31.6 m³/s for the testing dataset, respectively, and were lower than two models using AREA_SAR (Model 1) or MNDWI (Model 2) solely as inputs. In Experiment II, three models with AREA_SAR (Model 4), R_NIR/R_SWIR (Model 5), and a combination of AREA_SAR and R_NIR/R_SWIR (Model 6) as inputs were developed; the MRE and MAE for the model using AREA_SAR and R_NIR/R_SWIR (Model 6) were 0.25 and 56.5 m³/s, respectively, which outperformed the two models treating AREA_SAR (Model 4) or MNDWI (Model 5) as single types of inputs. In Experiment III, three models using AREA_SAR (Model 7), MNDWI, and R_NIR/R_SWIR (Model 8) and the combination of AREA_SAR, MNDWI and R_NIR/R_SWIR (Model 9) were built; combining all three types of satellite observations (Model 9) exhibited the highest accuracy, for which the MRE and MAE were 0.18 and 18.4 m³/s, respectively. The results of all three experiments demonstrated that integrating optical and microwave observations could improve the accuracy of streamflow estimates using a data-driven model; the proposed method has great potential for near-real-time estimations of flood magnitude or to reconstruct past variations in streamflow using historical satellite images in data-sparse regions. Full article

(This article belongs to the Special Issue Remote Sensing and GIS Technology Applications for Water Resources and Flood Risk Management in River Basin and Coastal Zones)

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19 pages, 4263 KiB

Open AccessArticle

Interpolating Hydrologic Data Using Laplace Formulation

by Tianle Xu, Venkatesh Merwade and Zhiquan Wang

Remote Sens. 2023, 15(15), 3844; https://doi.org/10.3390/rs15153844 - 2 Aug 2023

Cited by 1 | Viewed by 1290

Abstract

Spatial interpolation techniques play an important role in hydrology, as many point observations need to be interpolated to create continuous surfaces. Despite the availability of several tools and methods for interpolating data, not all of them work consistently for hydrologic applications. One of the techniques, the Laplace Equation, which is used in hydrology for creating flownets, has rarely been used for data interpolation. The objective of this study is to examine the efficiency of Laplace formulation (LF) in interpolating data used in hydrologic applications (hydrologic data) and compare it with other widely used methods such as inverse distance weighting (IDW), natural neighbor, and ordinary kriging. The performance of LF interpolation with other methods is evaluated using quantitative measures, including root mean squared error (RMSE) and coefficient of determination (R²) for accuracy, visual assessment for surface quality, and computational cost for operational efficiency and speed. Data related to surface elevation, river bathymetry, precipitation, temperature, and soil moisture are used for different areas in the United States. RMSE and R² results show that LF is comparable to other methods for accuracy. LF is easy to use as it requires fewer input parameters compared to inverse distance weighting (IDW) and Kriging. Computationally, LF is faster than other methods in terms of speed when the datasets are not large. Overall, LF offers a robust alternative to existing methods for interpolating various hydrologic data. Further work is required to improve its computational efficiency. Full article

(This article belongs to the Special Issue Remote Sensing and GIS Technology Applications for Water Resources and Flood Risk Management in River Basin and Coastal Zones)

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15 pages, 5156 KiB

Open AccessArticle

Predicting Water Quality Distribution of Lakes through Linking Remote Sensing–Based Monitoring and Machine Learning Simulation

by Mahdi Sedighkia, Bithin Datta, Parisa Saeedipour and Asghar Abdoli

Remote Sens. 2023, 15(13), 3302; https://doi.org/10.3390/rs15133302 - 27 Jun 2023

Cited by 1 | Viewed by 1210

Abstract

The present study links monitoring and simulation models to predict water quality distribution in lakes using an optimized neural network and remote sensing data processing. Two data driven models were developed. First, a monitoring model was established that is able to convert spectral images to TDS distribution. Moreover, a simulation model was developed to generate a TDS distribution map for unseen scenarios for which no spectral images are available. Outputs of the monitoring model were applied as the observations for training the simulation model. The Nash–Sutcliffe model efficiency coefficient (NSE) was utilized in the system performance measurement of the models. Based on the results in the case study, the monitoring model was sufficiently robust to convert the operational land imager spectral bands of Landsat 8 to the TDS distribution map. The NSE was more than 0.6 for the monitoring model, which confirms the predictive skills of the model. Furthermore, the simulation model was highly reliable in generating the TDS distribution map of the lakes. Three tests were carried out to demonstrate the reliability of the model. When comparing the results of the monitoring model and simulation model, an NSE of more than 0.6 was found for all the tests. It is recommendable to apply the proposed method instead of conventional hydrodynamic models that might be highly time consuming for simulating water quality parameters distribution in lakes. Low computational complexity is the main advantage of the proposed method. Full article

(This article belongs to the Special Issue Remote Sensing and GIS Technology Applications for Water Resources and Flood Risk Management in River Basin and Coastal Zones)

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